In today’s increasingly data-driven world, the need for accurate and reliable information is paramount. Across multiple industries and roles, organizations rely on accurate and reliable information to drive their operations and make informed decisions. Not only does it form the basis for informed decision-making, but it enables businesses to achieve a deeper understanding of their customers while helping to drive operational efficiency, mitigates risks, and provides a competitive advantage.
Inaccurate or unreliable data can lead to flawed decisions, ineffective strategies, operational inefficiencies, compliance issues, and missed opportunities, jeopardizing business success. Therefore, organisations that prioritize data accuracy and reliability, increase adoption of data driven practice to position themselves for success in an increasingly complex and competitive landscape.
With the need for good quality data becoming evident, many organisations have started Data Management projects that focus on improving their data by identifying and remediating data quality issues. Identifying issues and measuring the quality can be done using different dimensions, but one significant challenge that many businesses face in the Master Data Management field is the duplication of data and the resulting ambiguity it brings. Even when other aspects of the data are fully in order, this particular data quality problem can have big implications. Not only because it is sometimes hard to detect, but because it can be even harder to remediate.
The challenges of duplicate data
The presence of duplicate and ambiguous data can have far-reaching consequences. Often, it is present in numerous domains and across many, if not all, levels within organizations. Whether someone is directly or indirectly concerned with the data, as both creator or consumer, or at C-level or in the workplace, their efficiency and effectiveness can suffer greatly.
In industries that rely on Supply Chain Optimization, multiple internal and external systems contain information on spare parts that only differ ‘on paper’. Nuts and bolts that have the same size, width, and head can be interchangeable in practice, while each supplier brings a different part number. This redundant or unclear data can result in inefficient maintenance planning, leading to unnecessary downtime, suboptimal allocation of resources, under- or overstocking, and increased maintenance costs
Similarly, in Spend Analysis, inaccurate or duplicated spending data can make it difficult to identify cost-saving opportunities, negotiate favourable contracts, and optimize procurement processes, ultimately leading to higher expenses.
Perhaps the most clearcut examples are domains where the ambiguity is related to people. Names and initials can be abbreviated when a person only uses their first name, addresses can sometimes be written in different ways, family names can differ from maiden names, etc. When it comes to B2C and Marketing-related processes, unclear or duplicate customer data can hinder accurate segmentation, leading to ineffective marketing campaigns and wasted efforts in targeting the wrong customer segments. The issues might even be simpler than that, when multiple occurrences of the same person result in incorrect address information. It can result in inefficient lead generation, leading to wasted marketing resources, ineffective lead qualification, and missed sales opportunities.
Related to Healthcare, problems might even be more serious. Newly arriving ER patients might not be found in a hospital’s system, and previously recorded information can be missed. This can lead to potentially life-threatening situations. When for example patients receive a treatment with a certain medication, they are allergic to, or must undergo MRI scanning while having surgical implants from previous treatments.
The solution: Golden records
One overarching issue is that the existing solutions attempting to tackle data duplication and ambiguity, often fall short in terms of effectiveness, efficiency, complexity, or costs. Countless businesses therefore find themselves searching for a solution that integrates seamlessly with their existing infrastructure, delivers consistent performance and offers scalability without success.
To address this issue, Eraneos offers a comprehensive solution to transform the information into “Golden Records”. Often described as “the Single Source of Truth”, Golden Records represent the most accurate and consolidated view of entity instances. This approach involves merging data from multiple systems or eliminating duplicate entries within the same system, thereby providing organizations with a unified and trustworthy source of information. Leveraging our wealth of experience and expertise gained from previous success stories, Eraneos offers a solution that is highly configurable, dynamic, fast, and scalable. Using cutting edge Machine Learning techniques on a fully cloud based platform, we offer a faster and more agile solution than the competition.
In our follow-up articles we will dive deeper into the concept of Golden Records and illustrate its aspects: Data Cleaning, Entity Matching, and Attribute Survival. And furthermore, how these aspects are combined on top of a Data Lineage layer to provide the most appropriate representation of the Truth from any perspective. Do you want to learn more about our solution? Reach out to someone from our Data & AI team.
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